| import numpy as np, gymnasium as gym, ogbench |
| env = gym.make("cube-single-v0") |
| u = env.unwrapped |
| print("task_infos count:", len(getattr(u,"task_infos",[])) ) |
| for i,ti in enumerate(getattr(u,"task_infos",[])[:6]): |
| print(" task",i, {k:ti[k] for k in ti if k in ("task_name","task_id")}) |
| ob, info = env.reset(options=dict(task_id=1, render_goal=False)) |
| print("reset ob shape", np.asarray(ob).shape, "info keys", list(info.keys())) |
| goal = info.get("goal"); |
| print("goal shape", None if goal is None else np.asarray(goal).shape) |
| succ=0; steps=0 |
| for t in range(1001): |
| a = env.action_space.sample() |
| ob, r, term, trunc, info = env.step(a) |
| steps+=1 |
| if info.get("success"): succ=1 |
| if term or trunc: break |
| print("episode: steps",steps,"reward_last",float(r),"term",term,"trunc",trunc,"success_seen",succ,"info_keys",list(info.keys())) |
| |
| env2=gym.make("cube-single-singletask-task2-v0"); o,i=env2.reset() |
| o,r,te,tr,i=env2.step(env2.action_space.sample()) |
| print("singletask step reward",float(r),"info",list(i.keys())) |
|
|
